A hierarchical K-nearest neighbor approach for volume of tissue activated estimation

Abstract

Deep brain stimulation (DBS) is a surgical technique used to treat movement disorders. The volume of tissue activated (VTA) is a concept that partly explains the effects of DBS. Its visualization as part of anatomically accurate reconstructions of the brain structures surrounding the DBS electrode has been shown to have important clinical applications. However, the computation time required to estimate the VTA with traditional methods makes it unsuitable for practical applications. In this study, we develop a hierarchical K-nearest neighbor approach (HKNN) for VTA computation to address that hurdle. Our method reduces the time to estimate the VTA by four orders of magnitude, to hundredths of a second. In addition, it keeps the error with respect to the standard method for VTA estimation in the same range of that obtained with alternative machine learning approaches, such as artificial neural networks, without the limitations entailed by them.